




'''simple docstring'''


import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.generation import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )





@require_flax
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Any:
     __a								=       jnp.ones((batch_size, length)   ) / length
     return scores







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       None
     __a								=       20

     __a								=       self._get_uniform_logits(batch_size=2   ,				length=UpperCamelCase   )

     # tweak scores to not be uniform anymore
     __a								=       scores.at[1, 5].set((1 / length) + 0.1   )  # peak, 1st batch
     __a								=       scores.at[1, 10].set((1 / length) - 0.4   )  # valley, 1st batch

     # compute softmax
     __a								=       jax.nn.softmax(UpperCamelCase   ,				axis=-1   )

     __a								=       FlaxTemperatureLogitsWarper(temperature=0.5   )
     __a								=       FlaxTemperatureLogitsWarper(temperature=1.3   )

     __a								=       jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase   ,				scores.copy()   ,				cur_len=UpperCamelCase   )   ,				axis=-1   )
     __a								=       jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase   ,				scores.copy()   ,				cur_len=UpperCamelCase   )   ,				axis=-1   )

     # uniform distribution stays uniform
     self.assertTrue(jnp.allclose(probs[0, :]   ,				warped_prob_sharp[0, :]   ,				atol=1e-3   )   )
     self.assertTrue(jnp.allclose(probs[0, :]   ,				warped_prob_smooth[0, :]   ,				atol=1e-3   )   )

     # sharp peaks get higher, valleys get lower
     self.assertLess(probs[1, :].max()   ,				warped_prob_sharp[1, :].max()   )
     self.assertGreater(probs[1, :].min()   ,				warped_prob_sharp[1, :].min()   )

     # smooth peaks get lower, valleys get higher
     self.assertGreater(probs[1, :].max()   ,				warped_prob_smooth[1, :].max()   )
     self.assertLess(probs[1, :].min()   ,				warped_prob_smooth[1, :].min()   )







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       None
     __a								=       10
     __a								=       2

     # create ramp distribution
     __a								=       np.broadcast_to(np.arange(UpperCamelCase   )[None, :]   ,				(batch_size, vocab_size)   ).copy()
     __a								=       ramp_logits[1:, : vocab_size // 2] + vocab_size

     __a								=       FlaxTopKLogitsWarper(3   )

     __a								=       top_k_warp(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )

     # check that correct tokens are filtered
     self.assertListEqual(jnp.isinf(scores[0]   ).tolist()   ,				7 * [True] + 3 * [False]   )
     self.assertListEqual(jnp.isinf(scores[1]   ).tolist()   ,				2 * [True] + 3 * [False] + 5 * [True]   )

     # check special case
     __a								=       5
     __a								=       FlaxTopKLogitsWarper(top_k=1   ,				filter_value=0.0   ,				min_tokens_to_keep=3   )

     __a								=       np.broadcast_to(np.arange(UpperCamelCase   )[None, :]   ,				(batch_size, length)   ).copy()
     __a								=       top_k_warp_safety_check(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )

     # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
     self.assertListEqual((scores == 0.0).sum(axis=-1   ).tolist()   ,				[2, 2]   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       None
     __a								=       10
     __a								=       2

     # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
     __a								=       np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]   )   )

     __a								=       FlaxTopPLogitsWarper(0.8   )
     __a								=       np.exp(top_p_warp(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )   )

     # dist should be filtered to keep min num values so that sum is >= top_p
     # exp (-inf) => 0
     __a								=       np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]   )
     self.assertTrue(np.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )   )

     # check edge cases with negative and extreme logits
     __a								=       np.broadcast_to(np.arange(UpperCamelCase   )[None, :]   ,				(batch_size, vocab_size)   ).copy() - (
         vocab_size // 2
     )

     # make ramp_logits more extreme
     __a								=       ramp_logits[1] * 100.0

     # make sure at least 2 tokens are kept
     __a								=       FlaxTopPLogitsWarper(0.9   ,				min_tokens_to_keep=2   ,				filter_value=0.0   )
     __a								=       top_p_warp(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )

     # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
     self.assertListEqual((filtered_dist != 0.0).sum(axis=-1   ).tolist()   ,				[3, 2]   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       20
     __a								=       4
     __a								=       0

     __a								=       FlaxMinLengthLogitsProcessor(min_length=10   ,				eos_token_id=UpperCamelCase   )

     # check that min length is applied at length 5
     __a								=       ids_tensor((batch_size, 20)   ,				vocab_size=20   )
     __a								=       5
     __a								=       self._get_uniform_logits(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       min_dist_processor(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()   ,				4 * [-float('inf'   )]   )

     # check that min length is not applied anymore at length 15
     __a								=       self._get_uniform_logits(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       15
     __a								=       min_dist_processor(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     self.assertFalse(jnp.isinf(UpperCamelCase   ).any()   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       20
     __a								=       4
     __a								=       0

     __a								=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase   )

     # check that all scores are -inf except the bos_token_id score
     __a								=       ids_tensor((batch_size, 1)   ,				vocab_size=20   )
     __a								=       1
     __a								=       self._get_uniform_logits(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       logits_processor(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]   ).all()   )
     self.assertListEqual(scores[:, bos_token_id].tolist()   ,				4 * [0]   )  # score for bos_token_id shold be zero

     # check that bos_token_id is not forced if current length is greater than 1
     __a								=       3
     __a								=       self._get_uniform_logits(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       logits_processor(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     self.assertFalse(jnp.isinf(UpperCamelCase   ).any()   )







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       20
     __a								=       4
     __a								=       0
     __a								=       5

     __a								=       FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase   ,				eos_token_id=UpperCamelCase   )

     # check that all scores are -inf except the eos_token_id when max_length is reached
     __a								=       ids_tensor((batch_size, 4)   ,				vocab_size=20   )
     __a								=       4
     __a								=       self._get_uniform_logits(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       logits_processor(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]   ).all()   )
     self.assertListEqual(scores[:, eos_token_id].tolist()   ,				4 * [0]   )  # score for eos_token_id should be zero

     # check that eos_token_id is not forced if max_length is not reached
     __a								=       3
     __a								=       self._get_uniform_logits(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       logits_processor(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     self.assertFalse(jnp.isinf(UpperCamelCase   ).any()   )







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       4
     __a								=       10
     __a								=       15
     __a								=       2
     __a								=       1
     __a								=       15

     # dummy input_ids and scores
     __a								=       ids_tensor((batch_size, sequence_length)   ,				UpperCamelCase   )
     __a								=       input_ids.copy()

     __a								=       self._get_uniform_logits(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       scores.copy()

     # instantiate all dist processors
     __a								=       FlaxTemperatureLogitsWarper(temperature=0.5   )
     __a								=       FlaxTopKLogitsWarper(3   )
     __a								=       FlaxTopPLogitsWarper(0.8   )

     # instantiate all logits processors
     __a								=       FlaxMinLengthLogitsProcessor(min_length=10   ,				eos_token_id=UpperCamelCase   )
     __a								=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase   )
     __a								=       FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase   ,				eos_token_id=UpperCamelCase   )

     __a								=       10

     # no processor list
     __a								=       temp_dist_warp(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     __a								=       top_k_warp(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     __a								=       top_p_warp(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     __a								=       min_dist_proc(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     __a								=       bos_dist_proc(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
     __a								=       eos_dist_proc(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )

     # with processor list
     __a								=       FlaxLogitsProcessorList(
         [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]   )
     __a								=       processor(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )

     # scores should be equal
     self.assertTrue(jnp.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )   )

     # input_ids should never be changed
     self.assertListEqual(input_ids.tolist()   ,				input_ids_comp.tolist()   )







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       4
     __a								=       10
     __a								=       15
     __a								=       2
     __a								=       1
     __a								=       15

     # dummy input_ids and scores
     __a								=       ids_tensor((batch_size, sequence_length)   ,				UpperCamelCase   )
     __a								=       input_ids.copy()

     __a								=       self._get_uniform_logits(UpperCamelCase   ,				UpperCamelCase   )
     __a								=       scores.copy()

     # instantiate all dist processors
     __a								=       FlaxTemperatureLogitsWarper(temperature=0.5   )
     __a								=       FlaxTopKLogitsWarper(3   )
     __a								=       FlaxTopPLogitsWarper(0.8   )

     # instantiate all logits processors
     __a								=       FlaxMinLengthLogitsProcessor(min_length=10   ,				eos_token_id=UpperCamelCase   )
     __a								=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase   )
     __a								=       FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase   ,				eos_token_id=UpperCamelCase   )

     __a								=       10

     # no processor list
     def run_no_processor_list(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ):
       __a								=       temp_dist_warp(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
       __a								=       top_k_warp(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
       __a								=       top_p_warp(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
       __a								=       min_dist_proc(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
       __a								=       bos_dist_proc(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
       __a								=       eos_dist_proc(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
       return scores

     # with processor list
     def run_processor_list(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ):
       __a								=       FlaxLogitsProcessorList(
           [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]   )
       __a								=       processor(UpperCamelCase   ,				UpperCamelCase   ,				cur_len=UpperCamelCase   )
       return scores

     __a								=       jax.jit(UpperCamelCase   )
     __a								=       jax.jit(UpperCamelCase   )

     __a								=       jitted_run_no_processor_list(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
     __a								=       jitted_run_processor_list(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )

     # scores should be equal
     self.assertTrue(jnp.allclose(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )   )

     # input_ids should never be changed
     self.assertListEqual(input_ids.tolist()   ,				input_ids_comp.tolist()   )









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FNetForMaskedLM",
      "FNetForMultipleChoice",
      "FNetForNextSentencePrediction",
      "FNetForPreTraining",
      "FNetForQuestionAnswering",
      "FNetForSequenceClassification",
      "FNetForTokenClassification",
      "FNetLayer",
      "FNetModel",
      "FNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



